通过响应面方法 (RSM) 优化 AISI 316L 钢的喷丸强化参数:引入两个新的机械方面

IF 2.9 3区 工程技术 Q2 AUTOMATION & CONTROL SYSTEMS International Journal of Advanced Manufacturing Technology Pub Date : 2024-03-14 DOI:10.1007/s00170-024-13274-8
Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela
{"title":"通过响应面方法 (RSM) 优化 AISI 316L 钢的喷丸强化参数:引入两个新的机械方面","authors":"Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela","doi":"10.1007/s00170-024-13274-8","DOIUrl":null,"url":null,"abstract":"<p>The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing <i>P</i> leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating <i>C</i> promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% <i>C</i>, roughness peaks, exceeding the initial amount at 100% <i>C</i>. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at <i>P</i> = 6 bar and <i>C</i> = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.</p>","PeriodicalId":50345,"journal":{"name":"International Journal of Advanced Manufacturing Technology","volume":null,"pages":null},"PeriodicalIF":2.9000,"publicationDate":"2024-03-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimization of shot-peening parameters for steel AISI 316L via response surface methodology (RSM): introducing two novel mechanical aspects\",\"authors\":\"Pejman Ebrahimzadeh, Luis Borja Peral Martínez, Inés Fernández Pariente, Francisco Javier Belzunce Varela\",\"doi\":\"10.1007/s00170-024-13274-8\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing <i>P</i> leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating <i>C</i> promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% <i>C</i>, roughness peaks, exceeding the initial amount at 100% <i>C</i>. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at <i>P</i> = 6 bar and <i>C</i> = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.</p>\",\"PeriodicalId\":50345,\"journal\":{\"name\":\"International Journal of Advanced Manufacturing Technology\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":2.9000,\"publicationDate\":\"2024-03-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"International Journal of Advanced Manufacturing Technology\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1007/s00170-024-13274-8\",\"RegionNum\":3,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Advanced Manufacturing Technology","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00170-024-13274-8","RegionNum":3,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
引用次数: 0

摘要

本研究探讨了喷丸强化(SP)在 AISI 316L 不锈钢上的应用,以提高其机械性能。研究重点是优化喷丸参数--覆盖率(C)从 100% 到 4500%,喷丸速度(P)在 1.5 到 6 巴之间,其他喷丸因素保持不变--采用响应面方法(RSM)创建数学模型来准确分析数据。该模型探讨了初始配置之间的相互作用,以优化机械性能并提高当前钢材在 SP 表面处理后的性能。评估的性能包括累积压缩残余应力(CCRS)、累积半最大全宽(CFWHM)、奥氏体向马氏体的转变、显微硬度和表面粗糙度。通过 RSM 模型,除了显微硬度在 1.5 巴到 6 巴之间略有下降之外,P 的增加会导致各响应值的增加。C 升高会促进除粗糙度以外的响应值,直到 2300% 才开始下降,并达到最小值。在 4500% C 时,粗糙度达到峰值,超过了 100% C 时的初始值。期望的响应包括最大化 CCRS、CFWHM 和微硬度,同时最小化马氏体和粗糙度。对于所有响应中的相互作用,在 P = 6 巴和 C = 1860% 时,每个响应的值分别为 CCRS = 218 (MPa.mm)、CFWHM = 0.6871 (°.mm)、显微硬度 = 394 (HV)、马氏体转化率 = 48 (%) 和粗糙度 = 5.45 (µm)。通过比较 RSM 模型的最佳点,对实际测试中的响应进行重新评估,结果显示粗糙度的最小误差为 4.05,CCRS 的最大误差为 12.09。其他响应的误差介于此范围之间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Optimization of shot-peening parameters for steel AISI 316L via response surface methodology (RSM): introducing two novel mechanical aspects

The study explores the application of shot-peening (SP) on AISI 316L stainless steel to enhance mechanical properties. It focuses on optimizing SP parameters—coverage percentage (C) ranging from 100 to 4500% and shot velocity (P) between 1.5 and 6 bar while other SP factors were maintained constant—using response surface methodology (RSM) entails creating a mathematical model to analyze data accurately. This model explores interactions among initial configurations to optimize mechanical properties and enhance the performance of the current steel after the SP surface treatment. These properties evaluated include cumulative compressive residual stress (CCRS), cumulative full-width at half-maximum (CFWHM) newfangled factors for researchers to analyze, austenite transformation to martensite, micro-hardness, and surface roughness. Through the RSM model, increasing P leads to an increase in all response values in each one, except for microhardness, which registers a minor decrease from 1.5 to 6 bar. Elevating C promotes responses, excluding roughness, decreasing until 2300% and reaching its minimum. At 4500% C, roughness peaks, exceeding the initial amount at 100% C. In the optimization section, it seeks a passable value for each parameter. Desired responses involve maximizing CCRS, CFWHM, and micro-hardness while minimizing martensite and roughness. For interactions in all responses, at P = 6 bar and C = 1860%, values for each response were CCRS = 218 (MPa.mm), CFWHM = 0.6871 (°.mm), micro-hardness = 394 (HV), martensite conversion = 48 (%), and roughness = 5.45 (µm). Response reassessment in the real tests by comparison RSM model in optimal points showed a minimum error of 4.05 for roughness and a maximum error of 12.09 for CCRS. Other responses contained errors between this spectrum.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
CiteScore
5.70
自引率
17.60%
发文量
2008
审稿时长
62 days
期刊介绍: The International Journal of Advanced Manufacturing Technology bridges the gap between pure research journals and the more practical publications on advanced manufacturing and systems. It therefore provides an outstanding forum for papers covering applications-based research topics relevant to manufacturing processes, machines and process integration.
期刊最新文献
Pure niobium manufactured by Laser-Based Powder Bed Fusion: influence of process parameters and supports on as-built surface quality On a simulation-based chatter prediction system by integrating relative entropy and dynamic cutting force Modeling of the motorized spindle temperature field considering the thermos-mechanical coupling on constant pressure preloaded bearings Multi-layer solid-state ultrasonic additive manufacturing of aluminum/copper: local properties and texture Material-structure-process-performance integrated optimization method of steel/aluminum self-piercing riveted joint
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1